The team says that until ChEA was developed, no centralized database integrated results from, for instance, ChIP-seq and ChIP-chip experiments (these are used to identify how "transcription factor" proteins might regulate all genes in humans and mice). Now this new computational method should help streamline how scientists analyze these gene expression experiments.

"Our program allows researchers to identify which proteins are likely responsible for genetic changes that may cause disease," said Avi Ma'ayan, assistant professor of pharmacology and systems therapeutics at Mount Sinai. "Using our database, researchers will be able to better identify drug targets."

Yes, ChEA has already been put to the test in a few different case studies. One in particular highlights the potential usefulness of a free, centralized database.

Avi Ma'ayan
Mount Sinai School of Medicine

In the study, researchers analyzed two independent publications finding signature sets of genes that can differentiate between benign and malignant breast cancers. And yet the lists (one of 162 genes, the other of 73) did not match across these two studies. It, in fact, showed only 35 overlapping genes.

By running the two lists through ChEA, the team at Mount Sinai discovered that what the genes on both lists do share is a regulatory protein. In their study, the researchers reported: "The results from the ChEA analysis clearly implicate that TGF-beta/SMAD2/3 signaling plays a dominant role in breast cancer metastasis and can be used to further explain the origins of the discrepancy between the original two studies."

Ma'ayan says these kinds of discoveries are vital for understanding how diseases develop and thus how they might best be treated: "Our reanalysis of these two publications verifies the usefulness of this software and database in gaining a better mechanistic understanding of the role transcription factors play in controlling gene expression and, subsequently, the development of disease."